A refined Hilbert-Huang transform with applications to inter-area oscillation monitoring

Abstract

Abstract-This paper focuses on the refinement of standard Hilbert-Huang transform (HHT) technique to accurately characterize time varying, multicomponents interarea oscillations. Several improved masking techniques for empirical mode decomposition (EMD) and a local Hilbert transformer are proposed and a number of issues regarding their use and interpretation are identified. Simulated response data from a complex power system model are used to assess the efficacy of the proposed techniques for capturing the temporal evolution of critical system modes. It is shown that the combination of the proposed methods result in superior frequency and temporal resolution than other approaches for analyzing complicated nonstationary oscillations.

Identification of inter-area oscillations using wavelet transform and phasor measurement unit data

“…Following assumptions are considered in the simulations. It is assumed that inter‐area modes are dominant modes in the selected measurement in line with other measurement based methods for inter‐area oscillation identification . This can be carried out using participation analysis or analysis of the frequency of modes. The scale factor varies between 1 and 128 with single step. In each simulation, central frequency is set in the range of dominant mode frequency. In this approach, DC values of the signals are eliminated by calculation of the signal mean value.…”

“…Then methods based on the stationary signals analysis will have limitation in practical application and algorithms based on the non‐stationary signal analysis could be more useful. In , Hilbert‐Huang transform (HHT) technique has been employed to analysis of non‐stationary inter‐area oscillations. HHT is a non‐linear and non‐stationary transform, which has attracted a lot of interest in the recent years especially in analysis of medical and geophysics processes .…”

Harmonic Detection for Power Grids Using Adaptive Variational Mode Decomposition

“…The algorithm firstly uses empirical mode decomposition (EMD) algorithm to obtain the intrinsic mode function (IMF) components, and then the Hilbert transform is used to extract harmonic parameters. In practical applications, however, detection accuracy is considerably affected by noise, which causes serious mode-mixing problems [18][19][20]. The local mean decomposition (LMD) algorithm [21,22] uses division to replace the subtraction in the process of acquiring the IMF components in EMD algorithm.…”

Identification method for power system low‐frequency oscillations based on improved VMD and Teager–Kaiser energy operator

“…At present, there are many methods such as discrete Fourier transform (DFT) [5,6], wavelet-based methods [7,8], total leastsquares estimation of signal parameters via rotational invariance techniques (TLS-ESPRITs) [9], Hilbert transform (HT) [10], empirical mode decomposition (EMD) [11], Prony [12,13], recursive LS (RLS) [14], blind source separation [15], and stochastic subspace identification (SSI) [16], which are based on the measurement of trajectory data, to analyse low-frequency oscillation.…”